Looking beyond the mean: what within-person variability can tell us about dementia, cardiovascular disease and cystic fibrosis

纵向研究是指在一段时间内从相同的感兴趣单位或相同的个体收集相同信息的研究。例如，在对人类的纵向研究中，随着时间的推移，从相同的个体重复收集来自感兴趣的生物量（例如，认知功能、血压、肺活量）的信息。这使得我们能够了解这些量是如何随着时间的推移而平均变化的，以及测量值是如何随着时间的推移而变化和偏离每个人的平均轨迹的。传统上，对这些研究感兴趣的研究人员一直专注于人的平均曲线。例如，对血压的平均轨迹或认知功能的平均轨迹进行建模。然而，新兴的研究表明，一个人的平均曲线（个体内变异）的大偏差可能是潜在的病理过程的指示。例如，血压有时很高，有时很低的人可能患有一些需要注意的潜在疾病。同样地，那些记忆力有时很好，有时很差的人可能需要去看医生检查。因此，更好地了解个体内变异性可能会提供一个机会之窗，以更好地了解这些个体发生了什么，以及他们是否有更高的负面健康事件风险。我们已经确定了三个疾病领域，其中个体内变异性的研究是高度相关的：血压变异性及其与心血管疾病的联系，评估认知功能评分的变异性及其与痴呆的联系，以及肺功能变异性与囊性纤维化患者死亡率之间的联系。然而，现有的测量个体内变异性和识别可能与之相关的因素的方法非常有限。此外，在标准统计方法中忽略个体内变异或使用不适当的统计方法的影响仍然不清楚。对个体内变异性如何影响三个关注疾病领域常用统计方法产生的结果的不完全理解可能导致误导性结果。因此，我们的目标是产生关于个体内变异的知识，重点是三个关键的健康研究领域，心血管疾病，老龄化和痴呆症和囊性纤维化，这将有助于研究人员改善现有的知识。考虑到这一目标，我们建议：（1）评估现有的资源和统计方法，以了解个体内变异;（2）开发新的方法，以提高我们对个体内变异的理解，并将其应用于我们感兴趣的三个领域（心血管疾病、衰老和痴呆以及囊性纤维化）;（3）将新方法与统计方法联合收割机相结合，用于预测事件发生的概率，例如心血管事件，痴呆症诊断或死亡。我们将在领先的机构进行拟议的研究，包括剑桥大学MRC生物统计学单位和爱丁堡大学爱丁堡痴呆症预防研究小组。这项工作将在三个疾病领域的专家支持下，在各小组之间合作完成。我们将受益于这两个机构的经验丰富的团队和设施，以最大限度地增加向研究人员，从业人员和更广泛的研究社区传播我们的研究结果的机会。

Longitudinal studies are studies where the same information from the same units of interest or the same individuals are collected over time. For example, in longitudinal studies of human beings, information from biological quantities of interest (for instance, cognitive function, blood pressure, lung capacity) is collected repeatedly over time from the same individuals. This permits an understanding of how these quantities change over time on average for each individual, and also how measurements vary and deviate over time from each person's average trajectory.Traditionally, researchers interested in these studies have focused on the persons' average curve over time. For example, modelling the average trajectory of blood pressure or the average trajectory of cognitive functioning. However, emerging research suggests that large deviations from a person's average curve (intraindividual variability) may be indicative of an underlying pathological process. For example, individuals whose blood pressure is sometimes very high and some other times very low may be suffering from some underlying condition that may require attention. Similarly, those whose memory is sometimes very good and at other times very poor, may need to visit a doctor for a check-up. Hence, gaining a better understanding of intraindividual variability may offer a window of opportunity to better understand what is going on with these individuals and whether they are at higher risk of negative health events. We have identified three disease areas in which the study of intraindividual variability is highly relevant: blood pressure variability and the link with cardiovascular disease, variability in scores assessing cognitive function and their link with dementia, and the link between lung function variability and mortality in cystic fibrosis patients.However, existing methods to measure intraindividual variability and to identify factors that may be associated with it are very limited. Furthermore, the effect of ignoring intraindividual variability in standard statistical methods, or using inappropriate statistical methods, is still unclear. An incomplete understanding of how intraindividual variability affects results generated from commonly used statistical methods in the three disease areas of interest may result in misleading findings. Therefore, we aim to generate knowledge about intraindividual variability, focusing on the three critical health research areas, cardiovascular disease, ageing and dementia and cystic fibrosis, that will help researchers improve existing knowledge. With this aim in mind, we propose to: (1) evaluate existing resources and statistical methods to understand intraindividual variability; (2) develop new methods to improve our understanding of intraindividual variability and apply them to our three areas of interest (cardiovascular disease, ageing and dementia and cystic fibrosis); (3) combine the new methods with statistical methods for predicting the probability of an event occurring, such as a cardiovascular event, a dementia diagnosis or death.We will conduct the proposed research in leading institutions including the MRC Biostatistics Unit, University of Cambridge, and the Edinburgh Dementia Prevention Research group, University of Edinburgh. The work will be done collaboratively between the groups and supported by experts in the three disease areas. We will benefit from an experienced team and facilities in both institutions to maximise opportunities to disseminate our findings to researchers, practitioners and the wider research community.